This analysis is focusing on ride-sharing data by each city type, and want to show what is Average Fare per ride and driver. With the difference of each city what we can suggest to decision makers at Pyber.
- Figure 1. PyBer_Summary
- Figure 2. Total_Fare_by_City_Type
After running and looking at our data on this project, I like to suggest decrease supply of Drivers on Urban area. Urban area has highest demand (Highest Total Fares) but due to highest supply of Drivers average fare per driver is lowest. As reducing number of drivers, the company can increase average fare per driver that could make increasing company's profit. Second option can be increase driver on Rural area, since this market has highest Rides per Drivers. There is only 78 drivers while total of 125 rides (demands). So as increasing the drivers the company can fulfil the demand of the market. My third suggestion can resolve both Rural and Urban issue which we can accommodate Rural and Urban drivers. There is high demand on middle of Jan on Rural but low demand on Urban so the company can send drivers to Rural to consume the high demand. Also on March shows some high picks on Urban demand while Rural has slightly lower. So the company can move drivers each other to maximize Driver’s utilization and this can improve the company’s profit.